knitr::opts_chunk$set(
warning = FALSE, # show warnings during codebook generation
message = FALSE, # show messages during codebook generation
error = TRUE, # do not interrupt codebook generation in case of errors,
# usually better for debugging
echo = TRUE # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())
pander::panderOptions("table.split.table", Inf)
We collected the following data.
# omit the following lines, if your missing values are already properly labelled
codebook_data <- detect_missing(codebook_data,
only_labelled = TRUE, # only labelled values are autodetected as
# missing
negative_values_are_missing = FALSE, # negative values are missing values
ninety_nine_problems = TRUE, # 99/999 are missing values, if they
# are more than 5 MAD from the median
)
# If you are not using formr, the codebook package needs to guess which items
# form a scale. The following line finds item aggregates with names like this:
# scale = scale_1 + scale_2R + scale_3R
# identifying these aggregates allows the codebook function to
# automatically compute reliabilities.
# However, it will not reverse items automatically.
codebook_data <- detect_scales(codebook_data)
# Does your dataset have a name that is not reflected in the file name?
# Uncomment the line below and change the name
# metadata(codebook_data)$name <- "My Awesome Dataset"
codebook(codebook_data)
Dataset name: codebook_data
The dataset has N=8 rows and 3 columns. 0 rows have no missing values on any column.
Metadata for search engines
|
#Variables
Distribution of values for Alter
0 missing values.
| name | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | format.spss | label |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Alter | numeric | 0 | 1 | 21 | 42 | 77 | 44.5 | 20.78461 | ▇▂▅▁▅ | F8.0 | NA |
1
Distribution of values for Geschlecht
0 missing values.
| name | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | format.spss | label |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Geschlecht | numeric | 0 | 1 | 1 | 1.5 | 2 | 1.5 | 0.5345225 | ▇▁▁▁▇ | F8.0 | NA |
| name | value |
|---|---|
| maennlich | 1 |
| weiblich | 2 |
## No non-missing values to show.
8 missing values.
| name | data_type | n_missing | complete_rate | min | median | max | hist | format.spss | label |
|---|---|---|---|---|---|---|---|---|---|
| Bildungsniveau | numeric | 8 | 0 | NA | NA | NA | F8.2 | NA |
JSON-LD metadata
The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.
{
"name": "codebook_data",
"datePublished": "2025-05-09",
"description": "The dataset has N=8 rows and 3 columns.\n0 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name |label | n_missing|\n|:--------------|:-----|---------:|\n|Alter |NA | 0|\n|Geschlecht |NA | 0|\n|Bildungsniveau |NA | 8|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
"keywords": ["Alter", "Geschlecht", "Bildungsniveau"],
"@context": "http://schema.org/",
"@type": "Dataset",
"variableMeasured": [
{
"name": "Alter",
"@type": "propertyValue"
},
{
"name": "Geschlecht",
"value": "1. maennlich,\n2. weiblich",
"maxValue": 2,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "Bildungsniveau",
"@type": "propertyValue"
}
]
}`